import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

set_seed(7_7_0)


_SCREAMING_SNAKE_CASE 		=      {
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


_SCREAMING_SNAKE_CASE 		=      {
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

_SCREAMING_SNAKE_CASE 		=      os.path.dirname(os.path.abspath(__file__))
_SCREAMING_SNAKE_CASE 		=      os.path.join(os.path.expanduser("""~"""), """.cache""")
_SCREAMING_SNAKE_CASE 		=      os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCamelCase_							,					REMOTE_MODEL_PATHS[key]["""file_name"""]       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 os.makedirs(UpperCamelCase_							,					exist_ok=UpperCamelCase_       )
 hf_hub_download(repo_id=UpperCamelCase_							,					filename=UpperCamelCase_							,					local_dir=UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=False							,					UpperCamelCase_="text"       )		->	int:







 '''simple docstring'''
 if model_type == "text":
  UpperCamelCase			  =      BarkSemanticModel
  UpperCamelCase			  =      BarkSemanticConfig
  UpperCamelCase			  =      BarkSemanticGenerationConfig
 elif model_type == "coarse":
  UpperCamelCase			  =      BarkCoarseModel
  UpperCamelCase			  =      BarkCoarseConfig
  UpperCamelCase			  =      BarkCoarseGenerationConfig
 elif model_type == "fine":
  UpperCamelCase			  =      BarkFineModel
  UpperCamelCase			  =      BarkFineConfig
  UpperCamelCase			  =      BarkFineGenerationConfig
 else:
  raise NotImplementedError()
 UpperCamelCase			  =      f"""{model_type}_small""" if use_small else model_type
 UpperCamelCase			  =      REMOTE_MODEL_PATHS[model_key]
 if not os.path.exists(UpperCamelCase_       ):
  logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""       )
  _download(model_info["""repo_id"""]							,					model_info["""file_name"""]       )
 UpperCamelCase			  =      torch.load(UpperCamelCase_							,					map_location=UpperCamelCase_       )
 # this is a hack
 UpperCamelCase			  =      checkpoint["""model_args"""]
 if "input_vocab_size" not in model_args:
  UpperCamelCase			  =      model_args["""vocab_size"""]
  UpperCamelCase			  =      model_args["""vocab_size"""]
  del model_args["vocab_size"]

 # convert Bark model arguments to HF Bark model arguments
 UpperCamelCase			  =      model_args.pop("""n_head"""       )
 UpperCamelCase			  =      model_args.pop("""n_embd"""       )
 UpperCamelCase			  =      model_args.pop("""n_layer"""       )

 UpperCamelCase			  =      ConfigClass(**checkpoint["""model_args"""]       )
 UpperCamelCase			  =      ModelClass(config=UpperCamelCase_       )
 UpperCamelCase			  =      GenerationConfigClass()

 UpperCamelCase			  =      model_generation_config
 UpperCamelCase			  =      checkpoint["""model"""]
 # fixup checkpoint
 UpperCamelCase			  =      """_orig_mod."""
 for k, v in list(state_dict.items()       ):
  if k.startswith(UpperCamelCase_       ):
   # replace part of the key with corresponding layer name in HF implementation
   UpperCamelCase			  =      k[len(UpperCamelCase_       ) :]
   for old_layer_name in new_layer_name_dict:
    UpperCamelCase			  =      new_k.replace(UpperCamelCase_							,					new_layer_name_dict[old_layer_name]       )

   UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )

 UpperCamelCase			  =      set(state_dict.keys()       ) - set(model.state_dict().keys()       )
 UpperCamelCase			  =      {k for k in extra_keys if not k.endswith(""".attn.bias"""       )}
 UpperCamelCase			  =      set(model.state_dict().keys()       ) - set(state_dict.keys()       )
 UpperCamelCase			  =      {k for k in missing_keys if not k.endswith(""".attn.bias"""       )}
 if len(UpperCamelCase_       ) != 0:
  raise ValueError(f"""extra keys found: {extra_keys}"""       )
 if len(UpperCamelCase_       ) != 0:
  raise ValueError(f"""missing keys: {missing_keys}"""       )
 model.load_state_dict(UpperCamelCase_							,					strict=UpperCamelCase_       )
 UpperCamelCase			  =      model.num_parameters(exclude_embeddings=UpperCamelCase_       )
 UpperCamelCase			  =      checkpoint["""best_val_loss"""].item()
 logger.info(f"""model loaded: {round(n_params/1E6							,					1       )}M params, {round(UpperCamelCase_							,					3       )} loss"""       )
 model.eval()
 model.to(UpperCamelCase_       )
 del checkpoint, state_dict

 return model





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False							,					UpperCamelCase_="text"       )		->	Tuple:







 '''simple docstring'''
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 UpperCamelCase			  =      """cpu"""  # do conversion on cpu

 UpperCamelCase			  =      _get_ckpt_path(UpperCamelCase_							,					use_small=UpperCamelCase_       )
 UpperCamelCase			  =      _load_model(UpperCamelCase_							,					UpperCamelCase_							,					model_type=UpperCamelCase_							,					use_small=UpperCamelCase_       )

 # load bark initial model
 UpperCamelCase			  =      _bark_load_model(UpperCamelCase_							,					"""cpu"""							,					model_type=UpperCamelCase_							,					use_small=UpperCamelCase_       )

 if model_type == "text":
  UpperCamelCase			  =      bark_model["""model"""]

 if model.num_parameters(exclude_embeddings=UpperCamelCase_       ) != bark_model.get_num_params():
  raise ValueError("""initial and new models don't have the same number of parameters"""       )

 # check if same output as the bark model
 UpperCamelCase			  =      5
 UpperCamelCase			  =      10

 if model_type in ["text", "coarse"]:
  UpperCamelCase			  =      torch.randint(256							,					(batch_size, sequence_length)							,					dtype=torch.int       )
  UpperCamelCase			  =      bark_model(UpperCamelCase_       )[0]

  UpperCamelCase			  =      model(UpperCamelCase_       )

  # take last logits
  UpperCamelCase			  =      output_new_model_total.logits[:, [-1], :]

 else:
  UpperCamelCase			  =      3
  UpperCamelCase			  =      8
  UpperCamelCase			  =      torch.randint(256							,					(batch_size, sequence_length, n_codes_total)							,					dtype=torch.int       )

  UpperCamelCase			  =      model(UpperCamelCase_							,					UpperCamelCase_       )
  UpperCamelCase			  =      bark_model(UpperCamelCase_							,					UpperCamelCase_       )

  UpperCamelCase			  =      output_new_model_total.logits

 # output difference should come from the difference of self-attention implementation design
 if output_new_model.shape != output_old_model.shape:
  raise ValueError("""initial and new outputs don't have the same shape"""       )
 if (output_new_model - output_old_model).abs().max().item() > 1E-3:
  raise ValueError("""initial and new outputs are not equal"""       )

 Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
 model.save_pretrained(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					)		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      os.path.join(UpperCamelCase_							,					UpperCamelCase_       )

 UpperCamelCase			  =      BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase_							,					"""config.json"""       )       )
 UpperCamelCase			  =      BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase_							,					"""config.json"""       )       )
 UpperCamelCase			  =      BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase_							,					"""config.json"""       )       )
 UpperCamelCase			  =      EncodecConfig.from_pretrained("""facebook/encodec_24khz"""       )

 UpperCamelCase			  =      BarkSemanticModel.from_pretrained(UpperCamelCase_       )
 UpperCamelCase			  =      BarkCoarseModel.from_pretrained(UpperCamelCase_       )
 UpperCamelCase			  =      BarkFineModel.from_pretrained(UpperCamelCase_       )
 UpperCamelCase			  =      EncodecModel.from_pretrained("""facebook/encodec_24khz"""       )

 UpperCamelCase			  =      BarkConfig.from_sub_model_configs(
     UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 UpperCamelCase			  =      BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config							,					coarseAcoustic.generation_config							,					fineAcoustic.generation_config       )

 UpperCamelCase			  =      BarkModel(UpperCamelCase_       )

 UpperCamelCase			  =      semantic
 UpperCamelCase			  =      coarseAcoustic
 UpperCamelCase			  =      fineAcoustic
 UpperCamelCase			  =      codec

 UpperCamelCase			  =      bark_generation_config

 Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
 bark.save_pretrained(UpperCamelCase_							,					repo_id=UpperCamelCase_							,					push_to_hub=UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters

     parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
     parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()

     load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)

_SCREAMING_SNAKE_CASE 		=      9.8_06_65





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_ = g       )		->	float:







 '''simple docstring'''

 if fluid_density <= 0:
  raise ValueError("""Impossible fluid density"""       )
 if volume < 0:
  raise ValueError("""Impossible Object volume"""       )
 if gravity <= 0:
  raise ValueError("""Impossible Gravity"""       )

 return fluid_density * gravity * volume


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()
